We are rapidly evolving towards an AI-first world. Society is being overwhelmed by a digital content explosion, yet large scale processing of this content is still a challenge.

Thanks to a new technique called ‘deep learning‘ (read more) we can automatically derive actionable insights from this data.

We at Robovision (Ghent, Belgium) are working hard to create the scalable hardware and software to create valuable operational data from this digital explosion. We want to share this expertise with you and provide the tools necessary to enable AI for your business. We transform your data and turn them into a valuable asset.

We have proven our approach already in 3 verticals and we are open to other verticals.

Unleash the power of AI on your business:

We don’t sell. We co-create with you as a partner and add value to your existing business model. You have the cake, and together we put a cherry on it, making your products more integrated in an AI-first world. Request a trial for our AI as a service model (AI-a-a-S), in selected verticals.

We provide the complete pipeline. From data preparation to cloudification of your model.

A team of 13 leading experts (we’re hiring) can offer a wide variety of deep learning techniques and architectures. Contact us if you’re interested in a project management function @Robovision, we are looking for people with a track record.

We have an extensive background in general machine learning based image processing.

We are preferred partner of the big deep learning hardware providers (IBM, Nvidia). We specialize in configuring our elastically scalable software stack on these platforms.

DEEP LEARNING

Deep learning enables us to analyze and interpret large amounts of complex data by using vast amounts of virtual neurons stacked in layers on top of each other. Enjoy our deep learning as a service model in selected verticals and read more about our value proposition.

AI ROBOTS

For specific tracks related to hardware development, Robovision works together with Robovision Integrated Solutions to combine the power of AI with the next generation of robots, building cost-efficient prototypes in a limited amount of time.

“Robovision enabled us to create a genuinely interactive experience. Deep learning enables the robot to see and interact with our employees.”

Patrick Danau, CEO AUDI

HOW TO DISRUPT YOUR INDUSTRY?

We always start with an analysis of the use case. You are the specialist in your business, so together with you we analyze and format your data, to prepare it for the deep learning engine. We create a perspective of what a future data backbone will look like in your business.

Our team investigates how to manage this pipeline in your particular case.

In the second phase we adapt the RVAI (RoboVision Artificial Intelligence Engine) to the particular needs in your vertical. This means that we train pilot datasets, gradually facilitating the access to your team members.

In this phase we also start to use data augmentation (a type of multiplication of the dataset in order to limit the data labelling time).

In parallel, we discuss a custom, tailor made framework, compatible and competitive with your business model. This is exactly why we only focus on three verticals: we want to maximally align the incentive with you, together we will turn it into a success.

This phase usually takes 1 to 3 months, depending on the complexity of the data.

Now the real fun part starts. The tests on large scale datasets improve on a daily basis and our joint action teams start planning the scale up phase. Thanks to powerful partners as IBM and Amazon AWS and our proprietary parallelization schemes we are ready for prime time. As a privileged partner of IBM PowerAI we have the right contacts worldwide for quick upscaling and large scale integration.

The backbone is now solidly growing, and needs a proper face. Depending on the particulars of your business, we try to compactify the solution into an embedded device, or make custom user interfaces intended for B2C use. We also work together with partners to develop special apps that interface with RVAI.

Now that our first success is growing in the market, our teams are more and more connected and your organisation has benefited from a new data-oriented paradigm. We start thinking about a new innovation cycle (step 7).

BEHAVIOUR RECOGNITION AND DEEP LEARNING BIOMETRIC APPLICATIONS

CASE CARDS

To assemble a car, the car manufacturer needs a solid and validated flow of components. High tech scanning technology can guarantee that a specific compontent will arrive just in time on the production line. Audi relies on Robovision to fulfill this need.

By applying 3D deep learning on manufactured pieces Robovision checks if the right subtype is selected, and if a set of components is complete for a specific car type. In this case RVAI ensures that the WZB (the kit you use when you have a flat tyre) is complete and validated. Thanks to the strong GPUs from NVIDIA we keep the cycle time fast and snappy.

Pepsico manufactures and packages dorito chips in the highlands above Mexico City. With package branding changing so frequently, a powerful AI based central system is needed to teach branding very fast. The RVAI is the solution here. Reflective, blown up and unpredictable, recognizing orientation and branding at high speed is not for the faint hearted in this application.

The next horizon in our agricultural disruption is 3D. Natural products have a tendency to be highly unpredictable, in contrast to manufactured pieces. By configuring convolutional neural networks to 3D data we want to help the industry automate the handling of difficult products. A Jumbo 747 coming from Africa flies frequently to Amsterdam, full of Chrysanthemum stems. We want to grow them here in Western Europe and manipulate them with smart robots (that pick the stems from the mother plant. By doing so we can use the fertile ground of Ethiopia instead to feed the hungry in the region. The AI revolution can generate powerful secondary effects, disrupting the flow to cheap labour and creating a more harmonious world order.

We apply deep learning to processes. By mounting a series of proprietary sensors on machinery, we are able to generate data that are used as input for a deep learning architecture. In this way we can teach the system what normal stationary behaviour is and how to properly detect anomalies.